map_restaurant

#import resturant csv to my computer

data1 <- read.csv("/Users/lin/Desktop/Restaurant.csv")
head(data1)

class(data1$cuisines)

[1] "factor"

a <- as.character(data1$cuisines) 

#I am trying to identity which resturant is vegetarian

library("stringr")
vege <- str_detect(a,"Vegetarian")
data2 <- cbind(vege,data1)

#Filter out the restaurant which is not vegetarian

class(data2$vege)

[1] "logical"

data2$vege <- as.numeric(data2$vege)

#do the subset for vege

library(dplyr)

Registered S3 method overwritten by 'dplyr':
  method           from
  print.rowwise_df     

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

data3 <- subset(data2,vege==1) 

create a new dataset that I convert to lat and lon to the county level

library(dplyr)
data4 <- data3 %>% 
  select(id,city,name,latitude, longitude,phones,paymentTypes,postalCode) %>% 
  na.omit()
View(data4)

library(sp)
library(maps)
library(maptools)

Checking rgeos availability: TRUE

# The single argument to this function, pointsDF, is a data.frame in which:
#   - column 1 contains the longitude in degrees (negative in the US)
#   - column 2 contains the latitude in degrees

latlong2county <- function(pointsDF) {
    # Prepare SpatialPolygons object with one SpatialPolygon
    # per county
    counties <- map('county', fill=TRUE, col="transparent", plot=FALSE)
    IDs <- sapply(strsplit(counties$names, ":"), function(x) x[1])
    counties_sp <- map2SpatialPolygons(counties, IDs=IDs,
                     proj4string=CRS("+proj=longlat +datum=WGS84"))

    # Convert pointsDF to a SpatialPoints object 
    pointsSP <- SpatialPoints(pointsDF, 
                    proj4string=CRS("+proj=longlat +datum=WGS84"))

    # Use 'over' to get _indices_ of the Polygons object containing each point 
    indices <- over(pointsSP, counties_sp)

    # Return the county names of the Polygons object containing each point
    countyNames <- sapply(counties_sp@polygons, function(x) x@ID)
    countyNames[indices]
}

# Test the function using points in Wisconsin and Oregon.
testPoints <- data.frame(x = data4$longitude, y = data4$latitude)

county_list<- latlong2county(testPoints)

county_list_data <- as.data.frame(county_list)
data5 <- cbind(data4,county_list_data) #merge to data4 by adding a column named county_list

data5 <- data5 %>% 
  na.omit()
unique(data5$county_list)

 [1] new york,chautauqua  new york,albany      new york,saratoga   
 [4] new york,rensselaer  new york,washington  new york,ulster     
 [7] new york,warren      new york,westchester new york,kings      
[10] new york,queens      new york,new york    new york,cayuga     
[13] new york,bronx       new york,dutchess    new york,nassau     
[16] new york,ontario     new york,erie        new york,suffolk    
[19] new york,onondaga    new york,columbia    new york,franklin   
[22] new york,monroe      new york,orange      new york,oneida     
[25] new york,essex       new york,otsego      new york,greene     
[28] new york,putnam      new york,niagara     new york,delaware   
[31] new york,rockland    new york,clinton     new york,montgomery 
[34] new york,broome      new york,tompkins    new york,seneca     
[37] new york,sullivan    new york,madison     new york,st lawrence
[40] new york,steuben     new york,chemung     new york,herkimer   
[43] new york,chenango    new york,schenectady new york,hamilton   
[46] new york,livingston  new york,jefferson   new york,tioga      
[49] new york,cortland    new york,yates       new york,fulton     
[52] new york,wayne       new york,schuyler    new york,orleans    
[55] new york,cattaraugus new york,genesee     new york,lewis      
[58] new york,oswego     
58 Levels: new york,albany new york,bronx ... new york,yates

#regular expression for ny vege restaurant data

county_name <- as.character(data5$county_list)
# Remove all before and up to ",":
county_name2 <- gsub(".*,","",county_name)
data6 <- cbind(county_name2,data5)  
data6$county_list <- NULL
data6

unique(data6$county_name2)

 [1] chautauqua  albany      saratoga    rensselaer  washington  ulster     
 [7] warren      westchester kings       queens      new york    cayuga     
[13] bronx       dutchess    nassau      ontario     erie        suffolk    
[19] onondaga    columbia    franklin    monroe      orange      oneida     
[25] essex       otsego      greene      putnam      niagara     delaware   
[31] rockland    clinton     montgomery  broome      tompkins    seneca     
[37] sullivan    madison     st lawrence steuben     chemung     herkimer   
[43] chenango    schenectady hamilton    livingston  jefferson   tioga      
[49] cortland    yates       fulton      wayne       schuyler    orleans    
[55] cattaraugus genesee     lewis       oswego     
58 Levels: albany bronx broome cattaraugus cayuga chautauqua ... yates

#import election data

eleccounty<-read.csv("/Users/lin/Desktop/2016_US_County_Level_Presidential_Results.csv")

eleccounty$X1 <- NULL

eleccounty <- eleccounty %>% 
         filter(state_abbr=="NY") #there are 62 counties in NY State

#do some regular expression on election data

foo <- as.character(eleccounty$county_name)

foo2 <- gsub("\\s*\\w*$", "", foo)
foo3 <- tolower(foo2)
foo4 <- str_replace(foo3, "st. lawrence", "st lawrence")
foo4

 [1] "albany"      "allegany"    "bronx"       "broome"      "cattaraugus"
 [6] "cayuga"      "chautauqua"  "chemung"     "chenango"    "clinton"    
[11] "columbia"    "cortland"    "delaware"    "dutchess"    "erie"       
[16] "essex"       "franklin"    "fulton"      "genesee"     "greene"     
[21] "hamilton"    "herkimer"    "jefferson"   "kings"       "lewis"      
[26] "livingston"  "madison"     "monroe"      "montgomery"  "nassau"     
[31] "new york"    "niagara"     "oneida"      "onondaga"    "ontario"    
[36] "orange"      "orleans"     "oswego"      "otsego"      "putnam"     
[41] "queens"      "rensselaer"  "richmond"    "rockland"    "saratoga"   
[46] "schenectady" "schoharie"   "schuyler"    "seneca"      "st lawrence"
[51] "steuben"     "suffolk"     "sullivan"    "tioga"       "tompkins"   
[56] "ulster"      "warren"      "washington"  "wayne"       "westchester"
[61] "wyoming"     "yates"      

#regular expression of final step for election data

new_eleccounty <- cbind(eleccounty,foo4)

names(new_eleccounty)[names(new_eleccounty) == 'foo4'] <- 'county_name2'

#calculate who is win in 2016 on the county level

new_eleccounty <- new_eleccounty %>% 
  mutate(difference_in_vote=votes_gop-votes_dem) %>% 
  mutate (R.D= ifelse(difference_in_vote > 0, "Republican", "Democrat"))

#combine cleaned ny vege reastaurant with election county data in 2016

#left outer join
df <- left_join(data6, new_eleccounty,by = "county_name2")

Column `county_name2` joining factors with different levels, coercing to character vector

df$paymentTypes <- as.character(df$paymentTypes)
df$paymentTypes[df$paymentTypes==""] <- "NA"
df$paymentTypes <- as.factor(df$paymentTypes)

#make a leaflet map of vege restaurant in NY States

library(leaflet)

Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

library(RColorBrewer)

pal = colorFactor(palette = c("blue", "red"), domain = df$R.D) # Grab a palette
color_vote = pal(df$R.D)

content <- paste("ID:",df$id,"<br/>",
                 "County:",df$county_name,"<br/>",
                 "Restaurant Name:",df$name,"<br/>",
                 "Phones:",df$phones,"<br/>",
                 "PaymentType:",df$paymentTypes,"<br/>",
                 "PostCode",df$postalCode,"<br/>"
                 )
                

leaflet(df) %>%
  addProviderTiles("Stamen.TonerLite") %>% 
  # Using the Provider Name
  addCircles(color=color_vote,
             lng = ~longitude, 
             lat = ~latitude,
             popup = content,
             fillOpacity = 1,
             stroke=FALSE) %>% 
  addLegend(pal = pal, values = ~df$R.D, title = "Won Party in 2016") %>% 
  addCircleMarkers(clusterOptions = markerClusterOptions())

Assuming "longitude" and "latitude" are longitude and latitude, respectively

#make another graph (not useful, feel free to ignore it!)

as.character(df$county_name)

#install.packages("urbnmapr")
library(urbnmapr)
# Returns a ggplot2 object with a geom_map of the the lower 
# 48 states (plus Hawaii & Alaska reset on the bottom) 
uscounties_sf <- get_urbn_map("counties", 
                              sf = TRUE)

str(uscounties_sf)

Classes ‘sf’ and 'data.frame':  3142 obs. of  7 variables:
 $ county_fips: chr  "04015" "12035" "20129" "28093" ...
 $ state_abbv : chr  "AZ" "FL" "KS" "MS" ...
 $ state_fips : chr  "04" "12" "20" "28" ...
 $ county_name: chr  "Mohave County" "Flagler County" "Morton County" "Marshall County" ...
 $ fips_class : chr  "H1" "H1" "H1" "H1" ...
 $ state_name : chr  "Arizona" "Florida" "Kansas" "Mississippi" ...
 $ geometry   :sfc_MULTIPOLYGON of length 3142; first list element: List of 1
  ..$ :List of 1
  .. ..$ : num [1:1023, 1:2] -1321573 -1321334 -1320642 -1319546 -1317819 ...
  ..- attr(*, "class")= chr  "XY" "MULTIPOLYGON" "sfg"
 - attr(*, "sf_column")= chr "geometry"
 - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA
  ..- attr(*, "names")= chr  "county_fips" "state_abbv" "state_fips" "county_name" ...

counties <- counties %>% 
  filter(state_abbv=="NY")
counties

library(ggthemes)
elec_data <- inner_join(df, counties, by ="county_name")

Column `county_name` joining factor and character vector, coercing into character vector

#county_name" = "county_name")

winstatesmap <- ggplot(elec_data, 
    aes(x = long, y = lat, group=group)) + 
    geom_polygon(aes(fill = R.D), color="white")+
    scale_fill_manual(values = c("steel blue","firebrick"))+
    theme_map() + 
    coord_map(projection = "mercator")
winstatesmap